Description Usage Arguments Details Value Examples

The function estimates the Michaelis-Menten constant using progress-curve data, enzyme concentrations, substrate concentrations, and the catalytic constant.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | ```
MM_est(
method,
timespan,
products,
enz,
subs,
catal,
K_M_init,
std,
tun,
nrepeat,
jump,
burn,
K_M_m,
K_M_v,
volume,
t_unit,
c_unit
)
``` |

`method` |
This determines which model, the sQSSA or tQSSA model, is used for the estimation. Specifically, the input for method is TRUE (FALSE); then the tQSSA (sQSSA) model is used. |

`timespan` |
time points when the concentrations of products were measured. |

`products` |
measured concentrations of products |

`enz` |
initial enzyme concentrations |

`subs` |
initial substrate concentrations |

`catal` |
true value of the catalytic constant. |

`K_M_init` |
initial value of K_M constant for the Metropolis-Hastings algorithm. If the input is FALSE then it is determined by max(subs). |

`std` |
standard deviation of proposal distribution. If the input is FALSE then it is determined by using the hessian of log posterior distribution. |

`tun` |
tuning constant for the Metropolis-Hastings algorithm when std is FALSE (i.e., hessian of the log posterior distribution is used). |

`nrepeat` |
number of effective iteration, i.e., posterior samples. |

`jump` |
length of distance between sampling, i.e., thinning rate. |

`burn` |
length of burn-in period. |

`K_M_m` |
prior mean of gamma prior for the Michaelis-Menten constant K_M. If the input is FALSE then it is determined by max(subs). |

`K_M_v` |
prior variance of gamma prior for the Michaelis-Menten constant K_M. If the input is FALSE then it is determined by max(subs)^2*1000. |

`volume` |
the volume of a system. It is used to scale the product concentration. FALSE input provides automatic scaling. |

`t_unit` |
the unit of time points. It can be an arbitrary string. |

`c_unit` |
the unit of concentrations. It can be an arbitrary string. |

The function MM_est generates a set of Markov Chain Monte Carlo simulation samples from posterior distribution of the Michaelis-Menten constant of enzyme kinetics model. Because the function estimates only the Michaelis-Menten constant the true value of the catalytic constant should be given. Authors' recommendation: "Do not use this function directly. Do use the function main_est() to estimate the parameter so that the main function calls this function"

A vector containing posterior samples of the estimated parameter: the Michaelis-Menten constant.

1 2 3 4 5 6 7 8 9 10 11 | ```
## Not run:
data("timeseries_data_example")
timespan1=timeseries_data_example[,c(1,3,5,7)]
products1=timeseries_data_example[,c(2,4,6,8)]
MM_result <- MM_est(method=TRUE,timespan=timespan1,products=products1,
enz = c(4.4, 4.4, 440, 440), subs=c(4.4, 4.4, 4.4, 4.4), catal = 0.051,
K_M_init = 1, K_M_m = 1, K_M_v = 100000, std = 10, tun =3.5,
nrepeat = 1000, jump = 10, burn = 1000, volume = FALSE,
t_unit = "sec", c_unit = "mM")
## End(Not run)
``` |

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